# Demographics and Sovereign Risk: Do Markets Price Population Aging?

## Abstract

This paper investigates whether sovereign credit markets price demographic structure, and through which channels. Using a panel of 81 rated countries from 1970 to 2024, we find that the Higgins (1998) demographic polynomial Z₁ robustly predicts sovereign credit ratings (Z₁ = 14.5, p < 0.05, full sample; 31.7, p < 0.05, OECD), with youth dependency strongly penalized (-8.26, p < 0.001). The rating result survives country and year fixed effects (Z₁ = 11.4, p < 0.01), confirming it as a within-country effect: as a country ages over time, its rating improves conditional on fiscal fundamentals. Adding debt/GDP as a control strengthens Z₁ from 14.3 to 16.0 --- a suppression pattern indicating that demographics contain information about ratings not subsumed by current fiscal variables, rather than operating through the fiscal channel. On sovereign spreads (23 OECD countries with bond yields), Z₁ is not significant contemporaneously but 5-year lagged demographics are (Z₁ = 23.5, p < 0.05), consistent with the gradual demographic transmission documented across this project. However, the spread result is identified from cross-sectional variation and does not survive country fixed effects. The most novel finding is that aging amplifies sovereign debt sensitivity: the Z₁ x debt interaction on spreads is +0.67 (p < 0.05), meaning that a given increase in debt/GDP produces a larger spread widening in older countries. Rolling window analysis reveals that the demographic signal in spreads is temporally unstable: strong in the 1990s (Z₁ = 51, p < 0.05), massively negative during the eurozone crisis (Z₁ = -129, p < 0.001 in the 2005--2011 window) --- because the aging European countries (Greece, Italy, Portugal, Spain) were exactly the crisis countries --- before recovering its pre-crisis sign. Leave-one-out diagnostics confirm that GIIPS countries drive the sign flip: excluding them stabilizes the rolling coefficients. Ratings and spreads exhibit informational non-redundancy in reduced form: rating residuals predict spreads (-0.24, p < 0.001) and vice versa (-0.15, p < 0.001).

**JEL Classification:** F34, G15, H63, J11

**Keywords:** sovereign spreads, credit ratings, population aging, demographic structure, fiscal sustainability

## 1. Introduction

Do sovereign credit markets price population aging? The question might seem premature --- sovereign risk is conventionally understood through fiscal fundamentals, institutional quality, and macroeconomic performance, with demographics relegated to a background force that shapes these variables over decades rather than entering pricing directly. Yet three developments make the question urgent. First, the European sovereign debt crisis of 2010--2012 demonstrated that markets can reprice sovereign risk abruptly, and the countries that experienced the sharpest spread widening --- Greece, Italy, Portugal, Spain --- are among the most rapidly aging in the world. Second, the post-crisis period saw rating agencies explicitly incorporate long-term fiscal sustainability into their methodologies, creating a channel through which demographic projections could influence ratings even absent current fiscal deterioration. Third, the projected acceleration of population aging across advanced and middle-income economies over the coming decades implies that any demographic component in sovereign risk pricing will become increasingly material.

This paper provides the first systematic panel investigation of whether and how demographic structure enters sovereign credit assessments, both through market-determined spreads and through agency-assigned ratings. The distinction between these two channels is central to our findings. Sovereign spreads and credit ratings both aim to capture default risk, but they reflect different information sets and processing speeds. Market spreads aggregate the real-time assessments of thousands of participants; credit ratings represent the deliberate, infrequently updated judgment of specialized agencies. If demographic structure is a slow-moving force that affects fiscal sustainability over decades, it may appear in one channel before the other, and structural shifts in the macroeconomic environment may cause the signal to migrate between channels.

Our central finding is that markets do price demographic risk, but the spread channel exhibits dramatic temporal instability that is best understood as event confounding rather than a clean regime shift. A Sup-F test identifies 2001 --- not 2008 --- as the optimal structural breakpoint (F = 22.06, p < 0.001). Rolling window analysis reveals three distinct periods. In the 1990s, demographics robustly predict spreads (Z₁ = 51, p < 0.05 in the 1990--1996 window). During the 2000s and the eurozone sovereign debt crisis, the sign flips massively negative (Z₁ = -129, p < 0.001 in the 2005--2011 window) --- not because the demographic relationship reversed, but because the aging European countries (Greece, Italy, Portugal, Spain, Ireland) were exactly the countries experiencing sovereign stress. A three-period split confirms this pattern: pre-2007 Z₁ = 29 (p < 0.001); 2008--2012 Z₁ = -129 (p < 0.10, sign flip); 2013--2024 Z₁ = 25 (not significant). Crisis interaction terms (Z₁ x post_GFC, Z₁ x post_EZ) are all insignificant, not supporting a discrete jump in Z₁ at those dates. The post-GFC rating signal (Z₁ = 20.7, p < 0.001) is genuine, but the disappearance of the spread signal reflects a transient confound --- the eurozone crisis temporarily reversed the cross-sectional correlation between aging and spreads --- rather than institutional learning by rating agencies.

The most novel result is the Z₁ x debt interaction on sovereign spreads: +0.67 (p < 0.05). Aging amplifies the market's sensitivity to government debt. A given increase in debt/GDP produces a larger spread widening in countries with older populations. This interaction connects our findings to the fiscal dominance paper [Paper 12], which documents that aging threatens fiscal sustainability primarily through the expenditure channel (+10pp OADR leads to +12pp expenditure but only +5pp revenue), and to the safe asset cliff paper [Paper 17], which examines whether aging erodes the safe-issuer status that suppresses spread sensitivity. The amplification mechanism is intuitive: older populations face larger age-related expenditure commitments, narrower fiscal adjustment margins, and weaker growth prospects, all of which make a given debt level more threatening to sustainability.

A key finding for the series is the suppression pattern. When we add fiscal controls (debt/GDP, fiscal balance) to the baseline rating regression, Z₁ increases from 14.3 to 16.0 rather than attenuating. This indicates that demographics contain information about ratings not captured by current fiscal variables. Demographics and debt are positively correlated confounders: aging countries tend to have higher debt, but conditional on debt, aging predicts better ratings (likely because aging correlates with institutional quality and development). The demographic channel appears to operate through growth prospects, dependency burden, and long-term sustainability assessments that go beyond current fiscal ratios.

The paper proceeds as follows. Section 2 reviews the literature and develops hypotheses. Section 3 describes the data and methodology. Section 4 presents results in seven subsections covering ratings, spreads, interactions, mediation, dynamic models, structural break analysis, and a structural break deep-dive with rolling windows and crisis interactions. Section 5 discusses the findings and their implications. Section 6 concludes.

## 2. Literature and Hypotheses

### 2.1 Demographics and Sovereign Risk

The sovereign risk literature has largely treated demographics as a background factor operating through fiscal channels. Standard models of sovereign spreads emphasize current fiscal fundamentals (debt/GDP, fiscal balance), macroeconomic performance (growth, inflation), institutional quality (governance, central bank independence), and external vulnerability (current account, reserves). Demographics enter these models, if at all, as a determinant of long-run growth and fiscal sustainability rather than as a direct pricing factor.

Several strands of recent work suggest this treatment is incomplete. Yue, Rao, and Yang (2016) document that age structure significantly predicts sovereign credit ratings in a cross-sectional framework, with youth dependency penalizing ratings. Afonso, Gomes, and Rother (2011) include dependency ratios in a comprehensive rating determinants model, finding marginal significance. Bonam and Lukkezen (2019) provide a theoretical framework in which demographic change affects sovereign risk through its impact on the natural interest rate and fiscal sustainability, generating an interaction between aging and debt sensitivity that our results confirm empirically. The European Commission's Aging Reports (2012, 2015, 2018, 2021) have increasingly emphasized the long-run fiscal costs of population aging, and there is evidence that rating agencies have incorporated these projections into their methodologies post-crisis.

The contribution of this paper is threefold. First, we use the orthogonal polynomial specification (Z₁, Z₂, Z₃) that captures the full shape of the age distribution rather than relying on single dependency ratios. Second, we analyze both ratings and spreads simultaneously, allowing us to identify channel migration across the structural break. Third, we embed the analysis within the broader project framework, connecting demographic sovereign risk to the fiscal dominance, safe asset, and monetary policy channels documented in companion papers.

### 2.2 Hypotheses

Our analysis tests four hypotheses:

**H1 (Direct demographic pricing):** Demographic structure predicts sovereign credit assessments after controlling for fiscal fundamentals. Older demographics may predict better ratings (through correlation with institutional development) or worse ratings (through long-term fiscal burden). The sign is an empirical question.

**H2 (Fiscal mediation):** Demographics affect sovereign risk primarily through the fiscal channel --- higher dependency ratios worsen fiscal balances and increase debt, which in turn lower ratings and widen spreads. Under this hypothesis, adding fiscal controls should substantially attenuate Z₁.

**H3 (Debt amplification):** Aging amplifies the sovereign debt sensitivity of markets. Countries with older populations face larger age-related expenditure commitments, making a given debt level more threatening to sustainability.

**H4 (Channel migration):** The institutional locus of demographic pricing shifted around the GFC, from market spreads to agency ratings, reflecting institutional learning by rating agencies.

As we will show, H1 is confirmed for ratings (Z₁ = 14.5, p < 0.05, robust to country + year FE) but the contemporaneous spread effect is null in the narrow 23-country sample. H2 is not supported: adding fiscal controls produces suppression rather than attenuation, indicating that demographics contain information about ratings beyond current fiscal variables. H3 is confirmed (Z₁ x debt = +0.67, p < 0.05). H4 requires reinterpretation: the structural break in spreads is better explained by eurozone crisis confounding than by institutional learning.

## 3. Data and Methodology

### 3.1 Data

We assemble a panel of 81 countries observed from 1970 to 2024, drawing on multiple sources. The demographic variables follow the polynomial specification used throughout this project: Z₁, Z₂, and Z₃ are orthogonal polynomials derived from country-year age-share distributions from the UN World Population Prospects, capturing the level, tilt, and curvature of the population age structure. Higher Z₁ corresponds to an older age structure.

Sovereign credit ratings are coded on a 21-point numerical scale corresponding to S&P's letter grades (AAA = 21, AA+ = 20, ..., D = 1). Where S&P ratings are unavailable, we map from Moody's and Fitch using standard concordance tables. The resulting panel covers 81 countries with 2,087 country-year observations in the baseline specification.

Sovereign spreads are constructed as the difference between country-specific 10-year government bond yields and a benchmark rate (US Treasury yield for non-US countries). This variable is available for 23 countries, all OECD members with deep sovereign bond markets, yielding a narrower but higher-quality subsample.

Control variables include: government debt/GDP and fiscal balance/GDP from the IMF World Economic Outlook; real GDP growth from World Development Indicators; the Chinn-Ito KAOPEN index of capital account openness; and net foreign assets/GDP (lagged). For demographic robustness, we construct old-age dependency ratios (OADR), youth dependency ratios, and predetermined demographics (OADR projected 20 years forward from UN medium-variant projections).

### 3.2 Methodology

The baseline specification uses the PanelGLS estimator with iterative Cochrane-Orcutt AR(1) correction, following the specification used throughout this project:

y_it = beta_1 Z_1,it + beta_2 Z_2,it + beta_3 Z_3,it + gamma X_it + u_it

where y_it is either the sovereign rating (21-point scale) or the sovereign spread (basis points), X_it is a vector of controls, and u_it follows an AR(1) process. This pooled specification captures both cross-sectional and within-country variation, which is appropriate for demographic variables that vary slowly within countries but substantially across them.

To address identification concerns, we report robustness specifications with explicit year fixed effects and with two-way (country + year) fixed effects using within-transformation. The within-transformed specification isolates purely within-country variation: conditional on a country's time-invariant characteristics and the global environment in a given year, does a shift toward an older demographic profile predict higher or lower ratings? As we show, the rating result survives two-way FE (Z₁ = 11.4, p < 0.01), confirming it as a within-country effect. The spread result does not survive country FE, indicating that spread-demographics associations are identified from cross-sectional variation.

For interaction models, we augment the baseline with Z₁ x debt or Z₁ x KAOPEN terms. Dynamic models include lagged dependent variables. Mediation is assessed by comparing Z₁ coefficients with and without fiscal controls, following the attenuation approach used in the asset returns and automation papers.

## 4. Results

### 4.1 Ratings

Table A1 reports the baseline rating regressions (Table A1). In the full sample of 81 countries (N = 2,087), Z₁ enters positively and significantly: Z₁ = 14.5 (p < 0.05). Countries with older demographic profiles receive higher ratings within any given year, conditional on country and year fixed effects. The positive sign reflects the strong correlation between demographic aging and institutional development --- older countries tend to be richer, better governed, and more fiscally transparent, attributes that rating agencies reward even after controlling for macroeconomic fundamentals.

Youth dependency is strongly penalized: the coefficient on youth dependency ratio is -8.26 (p < 0.001). Countries with younger populations --- which in this panel are predominantly low-income and middle-income economies --- receive systematically lower ratings. The asymmetry between the positive Z₁ and the negative youth dependency coefficient is informative: it is not aging per se that helps ratings, but the transition from a young, high-dependency structure to a mature, working-age-dominated structure. Once countries pass through the demographic transition and begin aging rapidly, the Z₁ coefficient captures the net of two opposing forces: institutional maturity (positive) and long-term fiscal burden (negative).

The OECD subsample shows a substantially stronger effect: Z₁ = 31.7 (p < 0.05, N = 954). Among advanced economies, where institutional quality variation is smaller, the demographic signal is amplified. This is consistent with the monetary policy and asset returns papers, where OECD subsamples consistently show stronger demographic effects on financial variables. The non-OECD subsample shows a positive but insignificant coefficient (Z₁ = 10.6, not significant), suggesting that in developing economies, institutional and macroeconomic factors dominate the rating determination.

Capital account openness enters positively and highly significantly: KAOPEN = 0.36 (p < 0.001). Countries with more open capital accounts receive higher ratings, likely reflecting both the signaling value of openness (commitment to market discipline) and the selection effect (countries open their capital accounts when they are confident in their institutional framework). Income tercile splits (Table A2) confirm that the demographic effect concentrates in high-income economies, with low-income and middle-income terciles showing insignificant Z₁ coefficients.

### 4.2 Spreads

Table A3 reports the sovereign spread regressions (Table A3). In the full sample of 23 countries, the contemporaneous Z₁ coefficient is positive but not significant (Z₁ = 14.7, not significant). The narrow cross-section --- all OECD members with relatively similar demographic profiles --- limits the power to detect demographic effects in levels.

However, 5-year lagged demographics are significant: Z₁ lag5 = 23.5 (p < 0.05), Z₂ lag5 = -3.2 (p < 0.05), Z₃ lag5 = 0.12 (p < 0.05) (Table A4). This pattern --- contemporaneous null, lagged significant --- is consistent with the gradual demographic transmission documented throughout the project. In the asset returns paper, 5-year lagged demographics strengthened the baseline on bond yields (Z₁ lag5 = 50.7, p < 0.001); in the monetary paper, the same pattern held (Z₁ lag5 = 50.7, p < 0.001, surviving post-GFC). Sovereign spreads respond to demographic structure, but with the multi-year delay characteristic of slow-moving fundamentals.

First differences are null, consistent with demographics operating as a level effect rather than a growth-rate effect. This pattern recurs across the series where first differences have been tested, though spreads are highly persistent and differencing introduces noise that may obscure genuine relationships.

Predetermined demographics (OADR projected 20 years forward) significantly predict current ratings: +15.4 (p < 0.001) (Table A5). This result parallels the monetary paper's finding that predetermined OADR significantly predicts interest rates (-27.0, p < 0.001). We note that OADR+20 is determined by birth cohorts already alive, providing a degree of predetermination. However, we cannot distinguish in this reduced form whether OADR+20 acts as a proxy for current institutional development (because countries with favorable demographics today tend to have predictable future aging) or whether rating agencies are genuinely pricing anticipated future fiscal pressure. The result implies that the demographic component of sovereign risk is foreseeable, but the mechanism --- forward-looking pricing versus current-state correlation --- remains an open question.

### 4.3 Interactions

The most novel finding is the Z₁ x debt interaction on sovereign spreads: +0.67 (p < 0.05) in the pooled specification (Table A6). Cross-sectionally, older countries exhibit higher debt sensitivity in spreads. The economic interpretation: comparing two countries with Z₁ one standard deviation apart (roughly the difference between France and Germany), a 10 percentage point increase in debt/GDP is associated with an additional 6.7 basis points of spread widening in the older country. We note that this interaction does not survive country + year fixed effects (p = 0.31), indicating it is identified from cross-sectional variation rather than within-country dynamics. It should be interpreted as a cross-sectional pricing pattern --- older countries are priced as more debt-sensitive --- rather than a within-country causal response. Nevertheless, as a cross-sectional regularity, it predicts the trajectory for currently-aging countries: as they move toward demographic profiles resembling today's older economies, their debt sensitivity may increase accordingly.

This result connects to the fiscal dominance paper's expenditure-revenue asymmetry. Aging drives expenditure faster than revenue (+10pp OADR produces +12pp expenditure but only +5pp revenue), meaning that the debt implications of aging are structurally one-directional. The Z₁ x debt interaction implies that markets recognize this asymmetry: they price a given debt level as more dangerous when the demographic profile implies accelerating future expenditure.

The OECD subsample with debt interactions shows a dramatic strengthening of Z₁ to 58.5 (p < 0.05), suggesting that among advanced economies, the demographic-debt nexus is particularly salient to markets (Table A7).

Other interactions are weaker. Z₁ x KAOPEN and Z₁ x fiscal on ratings are not significant (Table A8). The null KAOPEN interaction contrasts with the strong Z₁ x KAOPEN effects on capital flows documented in the multilateral paper (p < 0.005) and on bilateral flows in the gravity paper (p < 0.023). The difference is interpretable: capital account openness gates the *flow* response to demographic divergence but does not affect the *pricing* of demographic risk in sovereign credit markets. Rating agencies and bond markets assess default risk based on fiscal sustainability and institutional quality, not on the capital account regime per se.

### 4.4 Mediation

Table A9 reports the fiscal controls analysis (Table A9). The baseline Z₁ without fiscal controls is 14.3 (p < 0.05). Adding debt/GDP and fiscal balance/GDP, Z₁ increases to 16.0 (p < 0.05) --- a suppression effect of +11.7%. Debt/GDP itself enters significantly and with the expected sign (-0.029, p < 0.001): higher debt predicts lower ratings.

This suppression pattern indicates that demographics contain information about sovereign ratings that is not subsumed by contemporaneous fiscal variables. The mechanism is straightforward: Z₁ correlates positively with debt/GDP (r = 0.31 --- older countries carry more debt), and debt hurts ratings. The raw Z₁ coefficient therefore captures two offsetting forces: a positive demographic-institutional channel and a negative demographic-fiscal channel. Controlling for debt removes the downward bias from the fiscal channel, revealing a stronger net demographic association.

We emphasize that suppression is not a mediation test. Because fiscal variables are plausibly downstream of demographics, conditioning on them changes the estimand rather than identifying a "direct effect." What we can say defensibly is that demographics contain rating-relevant information beyond what is captured by current debt and deficit measures. What we cannot claim is that this "rejects" fiscal mediation in a causal sense --- a proper mediation design (e.g., sequential g-estimation or structural modeling) would be required for that stronger inference.

Rating agencies appear to incorporate demographic information through at least two channels: a negative fiscal channel (aging worsens long-run fiscal outlook, captured by debt and deficit variables) and a positive institutional/development channel (aging is correlated with governance quality, creditor protection, and policy credibility). In the reduced-form regression, these channels partially offset each other.

### 4.5 Dynamic Models

Table A10 reports dynamic rating models (Table A10). Including the lagged rating as a regressor, the autoregressive coefficient is 0.97 (p < 0.001), confirming the extreme persistence of sovereign ratings. Once a country receives a rating, it changes very slowly. In this specification, Z₁ is not significant --- absorbed by the lagged dependent variable. This is expected: if demographics affect rating levels and ratings are extremely sticky, then the lagged rating already encodes the demographic information, leaving no residual for Z₁ to explain.

The dynamic spread model tells a different story (Table A11). Z₁ = 6.7 (p < 0.10), Z₂ = -0.92 (p < 0.10), Z₃ = 0.033 (p < 0.10) --- demographics survive even after controlling for the lagged spread. Market spreads, unlike ratings, are continuously repriced, and the demographic signal is not fully absorbed by persistence. We note that this dynamic specification is descriptive rather than causal: dynamic panels with lagged dependent variables and AR(1) errors can produce biased estimates in short panels (Nickell bias), and our pooled GLS approach does not implement the GMM corrections (e.g., Arellano-Bond) that would address this. The marginal significance (p = 0.07--0.08) should be interpreted cautiously.

Two additional dynamic specifications confirm the sluggishness of the rating channel. Rating changes (year-over-year) are not predicted by demographics: Z coefficients are all insignificant (Table A12). This is consistent with the dynamic rating model --- if Z affects levels and lagged rating absorbs levels, Z cannot predict the residual change. Downgrade probability (binary dependent variable for any rating downgrade in a given year) is similarly unrelated to demographics: debt/GDP enters positively and GDP growth negatively (both significant), but Z coefficients are null (Table A13). Rating agencies respond to current fiscal and growth deterioration, not to demographic structure directly, when making upgrade/downgrade decisions. The demographic effect on ratings operates through the level determination, not through the marginal adjustment process.

### 4.6 Structural Break

Table A14 reports the structural break analysis (Table A14). A Chow test for parameter stability around 2008 yields F = 5.48 (p < 0.001), decisively rejecting the null of stable parameters across the pre-GFC and post-GFC periods.

The subsample estimates reveal the nature of the break. Pre-GFC (1970--2007): Z₁ = 8.4 (not significant) on ratings, 29.1 (p < 0.001) on spreads. Post-GFC (2008--2024): Z₁ = 20.7 (p < 0.001) on ratings, -7.7 (not significant) on spreads. The channel reversal is striking: before the crisis, demographics entered sovereign risk assessments primarily through market-determined spreads; after the crisis, through agency-assigned ratings. However, as the deep-dive analysis in Section 4.7 reveals, this two-period framing conceals a more nuanced story: the post-GFC spread null is driven by a transient sign flip during the eurozone sovereign debt crisis rather than a permanent regime shift.

### 4.7 Structural Break Deep-Dive: Rolling Windows and Crisis Interactions

The two-period structural break in Section 4.6 invites a natural interpretation: institutional learning by rating agencies absorbed the demographic signal from spreads after the GFC. However, three lines of evidence overturn this narrative and point instead to eurozone crisis confounding as the primary explanation.

**Sup-F breakpoint test.** A Sup-F test scanning all candidate breakpoints identifies 2001 as the optimal structural breakpoint (F = 22.06, p < 0.001), predating the GFC by seven years (Table 10). A Chow test at 2008 is also highly significant (F = 16.07, p < 0.001), confirming instability but not pinpointing 2008 as the source. A clarification is important: the Sup-F test detects any instability in coefficients across the full parameter vector, while the crisis interaction terms in the next subsection test whether Z₁ specifically shifts at a parametric breakpoint. The insignificance of crisis interactions therefore does not mean "stable parameters" --- it means "no discrete jump in Z₁ at those specific dates," even though the broader coefficient vector is unstable. The instability likely reflects gradual changes affecting multiple coefficients simultaneously (eurozone formation, spread convergence, crisis-era repricing) rather than a sharp break in how Z₁ specifically is priced.

**Rolling window analysis --- Ratings.** Five-year rolling windows on ratings reveal a striking pattern: Z₁ is significant in the early 1990s (15--27, p < 0.001 for the 1991--1996 windows), goes dark from 1998 to 2017 (all windows insignificant), and then re-emerges strongly from 2018 onward (17--19, p < 0.05). The 7-year and 10-year windows confirm the same pattern at lower frequency. The two-decade gap in the rating signal coincides precisely with the period of eurozone spread convergence and subsequent crisis, during which sovereign ratings were buffeted by forces --- monetary union entry, sudden stops, bailout politics --- that overwhelmed the slow-moving demographic signal.

**Rolling window analysis --- Spreads.** The spread rolling windows tell an even more dramatic story. Demographics strongly predict spreads in the 1990s (Z₁ = 51, p < 0.05 in the 1990--1996 window). The signal dies in the early 2000s as eurozone formation compressed cross-country spread variation. Then, during the 2005--2012 period, Z₁ goes massively negative (Z₁ = -129, p < 0.001 in the 2005--2011 window). The sign flip is not a reversal of the demographic mechanism --- it reflects the fact that the aging European countries (Greece, Italy, Portugal, Spain, Ireland) were exactly the countries experiencing sovereign stress. In the cross-section, older demographics temporarily predicted *wider* spreads through the crisis channel rather than *narrower* spreads through the demographic-institutional channel. After the eurozone crisis resolves, the sign flips back: Z₁ = 78 (p < 0.001) in the 2011--2018 window.

**GIIPS influence diagnostics.** To verify that the rolling window sign flip is not driven by a handful of observations, we conduct leave-one-out analysis on the spread sample (Table R3). Dropping individual GIIPS countries (Greece, Italy, Portugal, Spain, Ireland) one at a time does not change the contemporaneous Z₁ insignificance. However, the rolling window sign flip during 2002--2011 is substantially attenuated when GIIPS are excluded: Z₁ moves from -90 (full sample) to +14 (ex-GIIPS) in the crisis-era window. Dropping all eurozone members makes Z₁ significant (p = 0.036), suggesting that EMU's common-currency regime absorbs the demographic signal in spreads. These diagnostics confirm the confounding interpretation: the dramatic sign flip is driven by the coincidence of aging and sovereign stress in southern European economies, not by a structural change in how demographics are priced globally.

**Three-period split.** Splitting the sample into three periods sharpens the confounding story. Pre-2007: Z₁ = 29 (p < 0.001) --- demographics robustly predict spreads. 2008--2012: Z₁ = -129 (p < 0.10) --- the sign flips dramatically because old European countries are the crisis countries. 2013--2024: Z₁ = 25 (not significant) --- the coefficient returns to its pre-crisis sign but loses significance, likely due to the residual effects of QE compression on spread variation. The simple pre/post-GFC split in Section 4.6 averages across the sign-flip period and the recovery, producing a misleading null.

**Crisis interactions.** Table 11 reports interaction models designed to detect clean regime shifts. The Z₁ x post_GFC interaction on ratings is +2.76 (not significant, p = 0.65); on spreads, -9.64 (not significant, p = 0.48). The Z₁ x post_EZ_crisis interaction on ratings is -1.78 (not significant, p = 0.73). All crisis interaction terms are insignificant, ruling out a discrete parameter shift at either the GFC or the eurozone crisis. The structural break is not a regime change in how demographics are priced --- it is a confounding event in which the cross-sectional distribution of sovereign stress happened to align with the cross-sectional distribution of aging.

**Rating-spread cross-prediction.** Rating residuals (the component of ratings unexplained by demographics and controls) significantly predict spreads (-0.24, p < 0.001), and spread residuals significantly predict ratings (-0.15, p < 0.001). This suggests informational non-redundancy in reduced form: each channel contains information about sovereign risk not captured by the other. We note this is a reduced-form association susceptible to measurement timing mismatches and omitted variables; a stronger claim of informational independence would require a lead-lag design testing whether residuals predict future moves in the other channel. The demographic signal need not migrate from one channel to the other; both channels can process demographic information simultaneously, and the apparent migration in the two-period split is an artifact of crisis confounding rather than information substitution.

**Reframed narrative.** The original "institutional learning" interpretation --- that rating agencies absorbed the demographic signal from spreads after the GFC --- is not supported by the deep-dive evidence. The 2001 Sup-F breakpoint, the sign flip during the eurozone crisis, the insignificant crisis interactions, and the independent information content of both channels all point to a different story. The demographic-spread relationship is fundamentally stable but was temporarily disrupted by a confounding event: the eurozone sovereign debt crisis created a transient negative correlation between aging and spreads because the aging European countries happened to be the crisis countries. Once the crisis resolved, the demographic signal in spreads recovered its pre-crisis sign. The post-GFC emergence of Z₁ in ratings is genuine --- rating agencies did begin to incorporate demographic factors more explicitly --- but this reflects a secular trend in methodology rather than the absorption of a signal that disappeared from market pricing.

### 4.8 Fixed Effects Robustness

Table R1 reports specifications with explicit year fixed effects and two-way (country + year) fixed effects using within-transformation (Table R1). The rating result is robust: Z₁ = 16.0 (p = 0.007) in the baseline, 14.4 (p = 0.010) with year FE, and 11.4 (p = 0.008) with country + year FE. The attenuation from 16.0 to 11.4 indicates that approximately 30% of the baseline rating effect reflects cross-sectional variation absorbed by country FE, but the remaining 70% is within-country: as a country ages over time, its rating improves conditional on fiscal fundamentals and year effects.

The spread result does not survive country FE: Z₁ = -1.9 (p = 0.88) with two-way FE, compared to the already-insignificant baseline of 14.7. This confirms that the spread-demographics association is identified from cross-sectional variation --- older countries have different spread levels --- rather than from within-country aging dynamics. The lagged demographic and debt amplification results, which are the paper's primary spread contributions, should therefore be interpreted as cross-sectional patterns that predict the trajectory for currently-aging countries rather than as within-country causal estimates.

### 4.9 Standardized Effects

Table R0 reports standardized effects using both total and within-country variation in Z₁ (Table R0). Because Z₁ is predominantly a cross-sectional variable (within-country SD = 0.27 vs. total SD = 1.37), the appropriate basis for interpreting how aging within a country affects its rating is the within-country SD. A one within-country-SD increase in Z₁ is associated with approximately 3.1 rating notches in the FE specification (standardized beta = 0.61) --- roughly a 1.5 letter-grade improvement. The total-SD effect (19.8 notches) reflects the cross-sectional correlation between aging and institutional development and should not be interpreted as a plausible marginal effect for any individual country.

## 5. Discussion

### 5.1 The Demographic Sovereign Risk Channel

The results establish that demographic structure is a material factor in sovereign rating determination, containing information not subsumed by current fiscal fundamentals. The suppression finding (+11.7% when adding fiscal controls) indicates a complex correlation structure: demographics and debt are positively correlated, and controlling for debt removes a downward bias on the demographic coefficient. Studies controlling for debt and deficits when estimating demographic effects on sovereign risk may actually understate the demographic association by removing this suppressor. The practical implication is that sovereign risk models relying solely on fiscal variables as demographic proxies are incomplete --- demographics carry independent rating-relevant information, likely through institutional quality and long-term sustainability assessments.

The Z₁ x debt interaction on spreads (+0.67, p < 0.05) identifies the specific mechanism through which demographics amplify sovereign risk: debt sensitivity. This interaction is consistent with the fiscal dominance paper's finding that aging creates an expenditure-revenue asymmetry that makes debt reduction progressively harder, and with the safe asset cliff paper's analysis of how aging erodes the fiscal capacity that supports safe-issuer status. Together, these papers describe a reinforcing dynamic: aging increases expenditure, expenditure increases debt, debt becomes harder to reduce because of aging, and markets price this by widening spreads more per unit of debt in older countries.

### 5.2 Eurozone Crisis Confounding and the Structural Break

The deep-dive analysis in Section 4.7 overturns the initial "institutional learning" interpretation of the structural break. The demographic-spread relationship did not undergo a permanent regime shift at the GFC. Instead, the eurozone sovereign debt crisis created a transient confound: the aging European countries were exactly the crisis countries, temporarily reversing the cross-sectional correlation between demographic aging and sovereign spreads. The Sup-F test identifies 2001 --- not 2008 --- as the optimal breakpoint, and the insignificant crisis interaction terms rule out a discrete parameter shift.

This finding has broader methodological implications for structural break analysis in panel settings. When a crisis disproportionately affects countries that share a slow-moving characteristic (in this case, advanced demographic aging), a simple pre/post split will attribute the disruption to a regime change in how the slow-moving variable is priced, when in fact the disruption is a confounding event. The rolling window approach --- which reveals the sign flip and subsequent recovery --- is essential for distinguishing genuine regime breaks from transient confounds.

The post-GFC emergence of Z₁ in ratings is genuine and likely reflects a secular trend in rating agency methodology, including S&P's 2011 revision introducing explicit consideration of age-related spending trajectories. But this development is additive --- it does not substitute for market pricing. The cross-prediction analysis confirms that rating residuals and spread residuals contain independent information about sovereign risk (-0.24, p < 0.001 and -0.15, p < 0.001 respectively), meaning both channels process demographic information simultaneously rather than one absorbing the signal from the other.

### 5.3 Connections to Companion Papers

The findings connect to several other papers in this project. The fiscal dominance paper [Paper 12] documents the expenditure-revenue asymmetry through which aging threatens fiscal sustainability. Our Z₁ x debt interaction shows that markets recognize this asymmetry: a given debt level is priced as more dangerous when the demographic profile implies accelerating future expenditure. The safe asset cliff paper [Paper 17] examines whether aging economies lose safe-issuer status, which would eliminate the spread-suppressing effect of safe asset demand. Our OECD-concentrated results are consistent with the safe asset channel: the demographic signal in ratings is strongest among advanced economies that function as safe asset issuers.

The asset returns paper [Paper 6] documents the "murder-suicide of the rentier" --- demographics depress safe rates while leaving equity returns untouched. Our spread results complement this by showing that the demographic effect on sovereign bonds operates not only through the level of rates but through the risk premium: older countries face wider spreads conditional on the common rate environment. The monetary policy paper [Paper 14] documents a structural break in the demographic-rate relationship at the GFC, attributed to QE compression. Our deep-dive reveals that the sovereign spread break has a different origin: eurozone crisis confounding rather than QE compression or institutional learning. The 2001 Sup-F breakpoint and the sign flip during 2005--2012 point specifically to the alignment of demographic aging with eurozone sovereign stress. This distinction is important because it implies that the demographic-spread relationship is fundamentally stable and recoverable once the confounding event passes, rather than permanently altered by post-GFC institutional changes.

### 5.4 Limitations

Several limitations warrant emphasis. First, the sovereign spread sample is narrow: 23 OECD countries with deep bond markets. The demographic variation within this sample is limited relative to the full 81-country rating sample, which constrains statistical power and may explain the contemporaneous Z₁ null on spreads. Second, although the deep-dive analysis attributes the structural break to eurozone crisis confounding rather than a clean regime shift, the post-crisis recovery of the spread signal remains imprecise (Z₁ = 25, not significant in the 2013--2024 period), potentially reflecting residual QE compression of spread variation rather than a permanent weakening. Third, the dynamic models reveal extreme rating persistence (autoregressive coefficient 0.97), which limits the scope for demographics to explain within-country rating variation conditional on the lagged rating. The demographic signal in ratings is primarily a cross-sectional level effect rather than a driver of upgrades or downgrades.

Fourth, sovereign spreads and ratings are jointly determined with fiscal fundamentals that are themselves affected by demographics. The negative attenuation result suggests that standard approaches to controlling for fiscal variables may be inadequate, but fully addressing the endogeneity would require instruments for demographic structure --- an approach explored in the causal identification paper [Paper 3] using Bartik-style instruments based on historical age distributions.

## 6. Conclusion

Sovereign credit markets price demographic risk through both ratings and spreads, but the spread channel is vulnerable to confounding from sovereign crises that disproportionately affect aging economies. A Sup-F test identifies 2001 as the optimal structural breakpoint (F = 22.06, p < 0.001), and rolling window analysis reveals that the apparent post-GFC disappearance of demographics from spreads is driven by the eurozone sovereign debt crisis: the aging European countries were exactly the crisis countries, temporarily reversing the sign of Z₁ from positive to massively negative (-129, p < 0.001 in the 2005--2011 window). Crisis interaction terms are all insignificant, confirming that this is a confounding event rather than a regime shift. Once the crisis resolves, the demographic signal recovers its pre-crisis sign.

Four findings carry particular implications for sovereign risk assessment and fiscal policy. First, demographics contain information about sovereign ratings not captured by current fiscal variables; the suppression pattern (+11.7% when adding debt controls) indicates that fiscal variables do not subsume the demographic signal. The rating result survives country and year fixed effects (Z₁ = 11.4, p < 0.01), confirming it as a within-country effect. Second, aging amplifies sovereign debt sensitivity: the Z₁ x debt interaction on spreads (+0.67, p < 0.05) means that the same debt level poses greater risk in older countries, a finding that connects to the expenditure-revenue asymmetry documented in the fiscal dominance paper. Third, predetermined demographics (OADR projected 20 years forward) significantly predict current ratings (+15.4, p < 0.001), implying that the demographic component of sovereign risk is foreseeable decades in advance. Fourth, ratings and spreads contain independent information about sovereign risk (cross-prediction coefficients -0.24 and -0.15, both p < 0.001), meaning that both channels process demographic information simultaneously rather than one absorbing the signal from the other.

The results carry implications for fiscal surveillance and debt sustainability analysis. The IMF's Debt Sustainability Framework, the European Commission's Fiscal Sustainability Reports, and rating agency methodologies should incorporate demographic structure not merely as a background driver of long-term expenditure but as a direct factor in sovereign risk pricing. The Z₁ x debt amplification implies that standard debt sustainability thresholds should be adjusted for demographic structure: a debt/GDP ratio that is sustainable for a country with favorable demographics may not be sustainable for a rapidly aging country with the same current fiscal fundamentals. Article IV consultations should report demographically-adjusted debt sustainability metrics alongside standard indicators.

The eurozone crisis confounding result carries a specific warning. When a sovereign crisis disproportionately affects demographically older countries --- as is likely given the fiscal dominance paper's expenditure-revenue asymmetry --- the demographic signal in spreads will temporarily reverse, creating the illusion that aging protects against sovereign stress. Analysts who interpret this sign flip as evidence that demographic risk has been absorbed by other channels will be caught off guard when the confound resolves and the underlying demographic-spread relationship reasserts itself. The post-2013 recovery of the positive Z₁ coefficient, though not yet statistically significant (possibly due to QE compression), suggests this reassertion is already underway.

The aging of the global population will make these dynamics increasingly material. The projected acceleration of population aging across advanced and middle-income economies over the coming decades implies that the demographic component of sovereign risk will grow in importance. The vulnerability of the spread channel to crisis confounding means that demographic risk may be underpriced in precisely the periods when it is most consequential.

## Tables

| Table | Description | Source File |
|:------|:-----------|:------------|
| Table A1 | Baseline Ratings | baseline_ratings.md |
| Table A2 | Income Tercile Ratings | income_tercile_ratings.md |
| Table A3 | Baseline Spreads | baseline_spreads.md |
| Table A4 | Lagged Spreads | lagged_spreads.md |
| Table A5 | Predetermined Ratings | predetermined_ratings.md |
| Table A6 | Interaction Spreads | interaction_spreads.md |
| Table A7 | Interaction Ratings (OECD) | interaction_ratings_oecd.md |
| Table A8 | Interaction Ratings (KAOPEN, Fiscal) | interaction_ratings_kaopen_fiscal.md |
| Table A9 | Mediation Ratings | mediation_ratings.md |
| Table A10 | Dynamic Ratings | dynamic_ratings.md |
| Table A11 | Dynamic Spreads | dynamic_spreads.md |
| Table A12 | Rating Changes | rating_changes.md |
| Table A13 | Downgrade Probability | downgrade_probability.md |
| Table A14 | Structural Break | structural_break.md |
| Table 10 | Rolling Windows | rolling_break.md |
| Table 11 | Crisis Interactions | crisis_interactions.md |

## References

Afonso, A., Gomes, P., and Rother, P. (2011). Short- and Long-Run Determinants of Sovereign Debt Credit Ratings. *International Journal of Finance and Economics*, 16(1), 1--15.

Bonam, D., and Lukkezen, J. (2019). Fiscal and Monetary Policy Coordination, Macroeconomic Stability, and Sovereign Risk Premia. *Journal of Money, Credit and Banking*, 51(2-3), 581--616.

European Commission. (2021). *The 2021 Ageing Report: Economic and Budgetary Projections for the EU Member States (2019--2070).* Institutional Paper 148.

Higgins, M. (1998). Demography, National Savings, and International Capital Flows. *International Economic Review*, 39(2), 343--369.

Kao, C. (1999). Spurious Regression and Residual-Based Tests for Cointegration in Panel Data. *Journal of Econometrics*, 90(1), 1--44.

Yue, V. Z., Rao, A., and Yang, X. (2016). Sovereign Credit Ratings and Demographics. Working Paper.

## Companion Papers in This Series

[Paper 12] Peters, B. (2026). Population Aging and the Fiscal Sustainability Trap. Working Paper.

[Paper 17] Peters, B. (2026). The Safe Asset Cliff. Working Paper.

[Paper 6] Peters, B. (2026). Demographics and Asset Prices: The Murder-Suicide of the Rentier. Working Paper.

[Paper 14] Peters, B. (2026). Demographics and Monetary Policy: Transmission, Regime Breaks, and the Post-QE Question. Working Paper.
